Wavefield simulation of 3D borehole dipole radiation
Bibliographic record
Abstract
ABSTRACT Reflections in acoustic borehole-logging data can be used to image near-borehole geologic structures, using either monopole or dipole measurements, the latter of which resolve azimuthal ambiguities. Dipole methods in particular can characterize small and subtle fractures in reservoir environments. Accurate tools for wavefield simulation are required to account for wave modes in and out of the borehole, and for the receiver responses to these modes. We have evaluated a 3D elastic staggered-grid finite-difference method for isotropic and anisotropic media in the context of the borehole acoustic imaging problem. Based on the convolutional perfectly matched layer and the multiaxial perfectly matched layer schemes, a hybrid perfectly matched layer method was developed and implemented to reduce artificial boundary reflections. The superiority of this hybrid perfectly matched layer beyond the convolutional perfectly matched layer and the multiaxial perfectly matched layer was proven based on numerical benchmarks in isotropic and anisotropic media. Numerical simulation of radiation, reflection, and multipole reception of elastic waves, excited by a dipole source, was carried out. To optimize azimuthal detection, the relationships between S-wave polarization as well as source-receiver offset and source-reflector angle was analyzed. Results indicated that the S-S reflection is most sensitive to the angle between the incident ray and the normal to the reflector. Its maximum amplitude occurs as the incident angle reaches its critical value, a fact that can be used to calculate the total propagation distance of the S-S wave. The critical angle as well as the SH-wave velocities of geologic structures outside the borehole can thus be determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".